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Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients

INTRODUCTION: COVID-19 pneumonia is characterized by ground-glass opacities (GGOs) and consolidations on Chest CT, although these CT features cannot be considered specific, at least on a qualitative analysis. The aim is to evaluate if Quantitative Chest CT could provide reliable information in discr...

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Autores principales: Caruso, Damiano, Polici, Michela, Zerunian, Marta, Pucciarelli, Francesco, Polidori, Tiziano, Guido, Gisella, Rucci, Carlotta, Bracci, Benedetta, Muscogiuri, Emanuele, De Dominicis, Chiara, Laghi, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Milan 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7548413/
https://www.ncbi.nlm.nih.gov/pubmed/33044733
http://dx.doi.org/10.1007/s11547-020-01291-y
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author Caruso, Damiano
Polici, Michela
Zerunian, Marta
Pucciarelli, Francesco
Polidori, Tiziano
Guido, Gisella
Rucci, Carlotta
Bracci, Benedetta
Muscogiuri, Emanuele
De Dominicis, Chiara
Laghi, Andrea
author_facet Caruso, Damiano
Polici, Michela
Zerunian, Marta
Pucciarelli, Francesco
Polidori, Tiziano
Guido, Gisella
Rucci, Carlotta
Bracci, Benedetta
Muscogiuri, Emanuele
De Dominicis, Chiara
Laghi, Andrea
author_sort Caruso, Damiano
collection PubMed
description INTRODUCTION: COVID-19 pneumonia is characterized by ground-glass opacities (GGOs) and consolidations on Chest CT, although these CT features cannot be considered specific, at least on a qualitative analysis. The aim is to evaluate if Quantitative Chest CT could provide reliable information in discriminating COVID-19 from non-COVID-19 patients. MATERIALS AND METHODS: From March 31, 2020 until April 18, 2020, patients with Chest CT suggestive for interstitial pneumonia were retrospectively enrolled and divided into two groups based on positive/negative COVID-19 RT-PCR results. Patients with pulmonary resection and/or CT motion artifacts were excluded. Quantitative Chest CT analysis was performed with a dedicated software that provides total lung volume, healthy parenchyma, GGOs, consolidations and fibrotic alterations, expressed both in liters and percentage. Two radiologists in consensus revised software analysis and adjusted areas of lung impairment in case of non-adequate segmentation. Data obtained were compared between COVID-19 and non-COVID-19 patients and p < 0.05 were considered statistically significant. Performance of statistically significant parameters was tested by ROC curve analysis. RESULTS: Final population enrolled included 190 patients: 136 COVID-19 patients (87 male, 49 female, mean age 66 ± 16) and 54 non-COVID-19 patients (25 male, 29 female, mean age 63 ± 15). Lung quantification in liters showed significant differences between COVID-19 and non-COVID-19 patients for GGOs (0.55 ± 0.26L vs 0.43 ± 0.23L, p = 0.0005) and fibrotic alterations (0.05 ± 0.03 L vs 0.04 ± 0.03 L, p < 0.0001). ROC analysis of GGOs and fibrotic alterations showed an area under the curve of 0.661 (cutoff 0.39 L, 68% sensitivity and 59% specificity, p < 0.001) and 0.698 (cutoff 0.02 L, 86% sensitivity and 44% specificity, p < 0.001), respectively. CONCLUSIONS: Quantification of GGOs and fibrotic alterations on Chest CT could be able to identify patients with COVID-19.
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spelling pubmed-75484132020-10-14 Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients Caruso, Damiano Polici, Michela Zerunian, Marta Pucciarelli, Francesco Polidori, Tiziano Guido, Gisella Rucci, Carlotta Bracci, Benedetta Muscogiuri, Emanuele De Dominicis, Chiara Laghi, Andrea Radiol Med Chest Radiology INTRODUCTION: COVID-19 pneumonia is characterized by ground-glass opacities (GGOs) and consolidations on Chest CT, although these CT features cannot be considered specific, at least on a qualitative analysis. The aim is to evaluate if Quantitative Chest CT could provide reliable information in discriminating COVID-19 from non-COVID-19 patients. MATERIALS AND METHODS: From March 31, 2020 until April 18, 2020, patients with Chest CT suggestive for interstitial pneumonia were retrospectively enrolled and divided into two groups based on positive/negative COVID-19 RT-PCR results. Patients with pulmonary resection and/or CT motion artifacts were excluded. Quantitative Chest CT analysis was performed with a dedicated software that provides total lung volume, healthy parenchyma, GGOs, consolidations and fibrotic alterations, expressed both in liters and percentage. Two radiologists in consensus revised software analysis and adjusted areas of lung impairment in case of non-adequate segmentation. Data obtained were compared between COVID-19 and non-COVID-19 patients and p < 0.05 were considered statistically significant. Performance of statistically significant parameters was tested by ROC curve analysis. RESULTS: Final population enrolled included 190 patients: 136 COVID-19 patients (87 male, 49 female, mean age 66 ± 16) and 54 non-COVID-19 patients (25 male, 29 female, mean age 63 ± 15). Lung quantification in liters showed significant differences between COVID-19 and non-COVID-19 patients for GGOs (0.55 ± 0.26L vs 0.43 ± 0.23L, p = 0.0005) and fibrotic alterations (0.05 ± 0.03 L vs 0.04 ± 0.03 L, p < 0.0001). ROC analysis of GGOs and fibrotic alterations showed an area under the curve of 0.661 (cutoff 0.39 L, 68% sensitivity and 59% specificity, p < 0.001) and 0.698 (cutoff 0.02 L, 86% sensitivity and 44% specificity, p < 0.001), respectively. CONCLUSIONS: Quantification of GGOs and fibrotic alterations on Chest CT could be able to identify patients with COVID-19. Springer Milan 2020-10-12 2021 /pmc/articles/PMC7548413/ /pubmed/33044733 http://dx.doi.org/10.1007/s11547-020-01291-y Text en © Italian Society of Medical Radiology 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Chest Radiology
Caruso, Damiano
Polici, Michela
Zerunian, Marta
Pucciarelli, Francesco
Polidori, Tiziano
Guido, Gisella
Rucci, Carlotta
Bracci, Benedetta
Muscogiuri, Emanuele
De Dominicis, Chiara
Laghi, Andrea
Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients
title Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients
title_full Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients
title_fullStr Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients
title_full_unstemmed Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients
title_short Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients
title_sort quantitative chest ct analysis in discriminating covid-19 from non-covid-19 patients
topic Chest Radiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7548413/
https://www.ncbi.nlm.nih.gov/pubmed/33044733
http://dx.doi.org/10.1007/s11547-020-01291-y
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